Jurafsky, Daniel & James H. Martin, N-gram Language Models., https:// web.stanford.edu/~jurafsky/slp3, 2022
Jurafsky, Daniel & James H. Martin, Vector Semantics and Embeddings, https:// web.stanford.edu/~jurafsky/slp3, 2022
Jurafsky, Daniel & James H. Martin, Neural Networks and Neural Language Models, https:// web.stanford.edu/~jurafsky/slp3, 2022
Jurafsky, Daniel & James H. Martin, Sequence Labeling for Parts of Speech and Named Entities, https:// web.stanford.edu/~jurafsky/slp3, 2022
Complementary Bibliography
Baroni, Marco, Raffaella Bernardi & Roberto Zamparelli, Frege in space: A program for compositional distributional semantics, University of Colorado Boulder, 2014
Baroni, Marco, Georgiana Dinu & Germán Kruszewski, Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors, Association for Computational Linguistics, 2014
Church, Kenneth Ward, Zeyu Chen & Yanjun Ma, Emerging trends: A gentle introduction to fine-tuning, Cambridge University Press, 2021
Devlin, Jacob, Ming-Wei Chang, Kenton Lee & Kristina Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Association for Computational Linguistics, 2019
Erk, Katrin, Vector space models of word meaning and phrase meaning: A survey, Wiley & Sons, 2012
Hirschberg, Julia & Manning, Christopher D., Advances in natural language processing, AAAS, 2015
Linzen, Tal, Issues in evaluating semantic spaces using word analogies, Association for Computational Linguistics, 2016
Lenzi, Alessandro, Distributional Models of Word Meaning., Annual Review of Linguistics, Vol. 4, 2018
Mikolov, Tomas, Wen-tau Yih & Zweig Geoffrey, Linguistic Regularities in Continuous Space Word Representations, Association for Computational Linguistics, 2013
Taher Pilehvar, Mohammad & Jose Camacho-Collados, José, Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning, MIT Press, 2021